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kasunw/
mental-health-mistral-7b-instructv0.2-finetuned-V2

Name: mental-health-mistral-7b-instructv0.2-finetuned-V2
Availability: InStock

generated_from_trainer

mistralai/Mistral-7B-Instruct-v0.2

adapter

apache-2.0

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mental-health-mistral-7b-instructv0.2-finetuned-V2

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the mental_health_counseling_conversations dataset. It achieves the following results on the evaluation set:

Loss: 0.6476

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The training and inference procedure can be found in this notebook

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.4315	1.0	352	0.9047
1.2645	2.0	704	0.7044
1.1876	3.0	1056	0.6476

Framework versions

PEFT 0.10.0
Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel

base_model = "mistralai/Mistral-7B-Instruct-v0.2"
adapter = "kasunw/mental-health-mistral-7b-instructv0.2-finetuned-V2"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    base_model,
    add_bos_token=True,
    trust_remote_code=True,
    padding_side='left'
)

# Create peft model using base_model and finetuned adapter
config = PeftConfig.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
                                             load_in_4bit=True,
                                             device_map='auto',
                                             torch_dtype='auto')
model = PeftModel.from_pretrained(model, adapter)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()

# Prompt content:
messages = [
    {"role": "user", "content": "Hey Connor! I have been feeling a bit down lately.I could really use some advice on how to feel better?"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages,
                                          tokenize=True,
                                          add_generation_prompt=True,
                                          return_tensors='pt').to(device)
output_ids = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, pad_token_id=2)
response = tokenizer.batch_decode(output_ids.detach().cpu().numpy(), skip_special_tokens = True)

# Model response: 
print(response[0])